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Dystocia

Dystoccia is a complication of childbirth characterized by difficult or abnormal labor.
It can be caused by a variety of factors, such as a large fetus, abnormal fetal positioning, or pelvic abnormalities.
Dystoccia can lead to prolonged labor and increased risk of maternal and fetal complications.
Prompt recognition and appropriate management are crucial to ensure the safety of both the mother and the baby.
PubCompare.ai's AI-driven platform can help researchers streamline their studies on dystoccia by providing easy access to relevant protocols and intelligent comparisons to identify the best approaches.

Most cited protocols related to «Dystocia»

1
Maternal near-miss study in Ethiopia
The study was conducted in six (6) hospitals in Tigray, Ethiopia, from January 30 to March 30, 2016. Hospitals were randomly selected from 16 public hospitals in the region[14 ]. The study was a facility-based, unmatched case control design. Sample size was estimated using a double population proportion formula based on a study from Morocco that showed hypertensive disease contributing the most to MNM [15 (link)]. Based on the Morocco study, we hypothesized the proportion of chronic hypertension to be double in cases (63.9%) and controls (47%) at a 95% confidence level and 80% power of the test, with a 1:2 ratio for cases and controls. Final sample size was 308, of which 103 were cases and 205 controls.
We considered MNM as a condition meeting any of the five disease-specific criteria proposed by Filippi [16 (link)]. In sampled hospitals, using medical notes, any woman diagnosed with at least one of the following complications was considered as a case: severe obstetric hemorrhage leading to shock; hypertensive diseases of pregnancy, including eclampsia and severe preeclampsia; dystocia, including uterine rupture and impending rupture; infections, including hyper- or hypothermia or a clear source of infection and clinical signs of shock, and; anemia, including low hemoglobin (<6 g/dl) or clinical signs of severe anemia in women without hemorrhage. Women not meeting the above criteria were considered as controls. Cases were sequentially recruited whereas controls were selected through systematic sampling. Data was collected using a structured questionnaire, administered in-person by nurse midwives. Socio-demographic characteristics, obstetric history, and knowledge of pregnancy-related danger signs were collected.
Questionnaire was based on tools validated by the World Health Organization (WHO) and in different literature and adapted to include context-specific factors [11 –13 , 15 (link), 17 (link)]. Questionnaire was prepared in English, translated to Tigrigna, and back-translated to English separately by two individuals to ensure consistency. Data was collected by 12 nurse midwives with experience in obstetric care. Data collection was supervised and data checked for consistency and completeness. Incomplete and unclear questionnaires were returned to interviewers to be completed.
Mekango D.E., Alemayehu M., Gebregergs G.B., Medhanyie A.A, & Goba G. (2017). Determinants of maternal near miss among women in public hospital maternity wards in Northern Ethiopia: A facility based case-control study. PLoS ONE, 12(9), e0183886.
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Publication 2017
Anemia Dystocia Eclampsia Hemoglobin Hemorrhage High Blood Pressures Infection Interviewers Nurse Midwife Pre-Eclampsia Pregnancy Shock Uterine Rupture Woman
2
High-Resolution Fetal Brain MRI Imaging

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Publication 2013
Autopsy Biologic Preservation Body Size Brain Brain Diseases Cadaver Care, Prenatal Central Nervous System Cerebrum Corpus Callosum Cortex, Cerebral Developmental Disabilities Dystocia Ethical Review Fetal Death Fetus Foot Formalin Head Males Menstruation Mothers Parent Pregnancy Respiration Disorders Ribs Specimen Collection Spontaneous Abortion Ultrasonography Ventricle, Lateral Woman
3
Induction of Labor with Oxytocin in Ethiopia
The study was conducted in JUSH found in Jimma town (352 km southwest of Addis Ababa). It is the only teaching and referral hospital in the southwestern part of the country. The hospital has a bed capacity of 450 of which 52 beds belong to the obstetric ward. The department gives 24 hours service of attending normal and abnormal labor including emergency cesarean section and laparotomy. During the study period, there were seven consultant gynecologists, 20 residents of four groups starting from 1st year to 4th year, varying numbers of medical interns, six midwives, and 20 clinical nurses. The hospital has a total of more than 750 staff (supportive and technical). It provides services for approximately 9000 inpatient and 80,000 outpatient attendances coming to the hospital from the catchment population of about 15,000,000 a year.
A cross-sectional study was conducted by reviewing records of term and post-term deliveries from September 1st, 2009 to August 31st, 2011. Hence, the source population was comprised of women who were admitted at term and post-term to the maternity and labor wards of the hospital during the study period. The study population included all women with term and post-term pregnancy who were admitted to the maternity and labor wards of the hospital and had induction with oxytocin during the study period.
Data were collected through review of medical records using a checklist which was prepared after reviewing different relevant literatures (6 (link), 8 , 11 (link), 12 (link) & 13 (link)). Patient cards, labor ward log books, discharge log books, operation
room log books, and medical interns' reporting log books were reviewed to collect the required data. Data were collected by 10 trained medical interns and two first year residents under supervision of the principal investigator. All collected data were reviewed by the principal investigator for their completeness on daily basis.
Data were cleaned and entered into computer software to be analyzed using SPSS version 16.0. Results were presented using tables where frequencies, percentages and mean ± SD were used whenever appropriate. Normally distributed continuous variables were compared using independent t-test. Bivariate analysis was carried out to determine associations of selected variables with the dependent variable and p-value of less than or equal to 0.25 was taken as a cut-off point to select candidate variables for the final multiple logistic regression models. Independent predictors were determined using adjusted odds ratio with 95% confidence interval in multiple regression analysis at p-value < 0.05.
Prior to data collection, ethical clearance was obtained from Ethical Clearance Committee of the College of Public Health and Medical Sciences of Jimma University. Additionally, confidentiality of patient information was ensured by omitting their names and using card numbers instead.
In this study, outcome of induction was defined as successful if a woman delivered vaginally either spontaneously or by an instrument after induction with oxytocin. Failed if a woman delivered by C/S due to failure to acquire either adequate uterine contraction (≥3 contractions or contractions lasting ≥40 seconds in ten minutes period) or failed to show favorable cervical changes (reach at least 4cm in dilatation and fully effaced) despite being on oxytocin drip for at least six hours or diagnosed as failed induction by the treating resident.
Table on labor induction protocol with oxytocin for all pregnant women in JUSH, using 6 units of oxytocin in 1000ml of IV fluids, start at 6 mu/min, increasing every 20 minutes (Table 1).
Girma W., Tseadu F, & Wolde M. (2016). Outcome of Induction and Associated Factors among Term and Post-Term Mothers Managed at Jimma University Specialized Hospital: A Two Years' Retrospective Analysis. Ethiopian Journal of Health Sciences, 26(2), 121-130.
Publication 2016
Cesarean Section Consultant Dilatation Dystocia Emergencies Gynecologist Hospital Referral Inpatient Labor, Induced Laparotomy Midwife Neck Neoplasm Metastasis Nurses Obstetric Delivery Obstetric Labor Outpatients Oxytocin Patient Discharge Patients Pregnancy, Prolonged Pregnant Women Supervision Uterine Contraction Woman
4
Adapting E-Commerce Solutions for Agriculture
Several solutions from the e-Commerce domain can also be utilized in the agriculture sector [28 (link)]. For example, IoT-based applications for safe transportation of sensitive pharmaceutical products in the context of e-Commerce are relevant to similar solutions for the transportation of sensitive agricultural goods [29 ,30 ]. Other popular examples include applications that use ML-based network monitoring tools to detect and classify malicious operations, such as information tampering and DoS attacks. [31 ,32 ]. Network traffic classification solutions for IoT systems can also be adapted to be used in agriculture or other sectors since network traffic attributes have similarities across different domains [33 (link)].
A part of the current research work has also been implemented in the context of the ENSURESEC project as a subcomponent of a communications monitoring toolset in an e-Commerce environment. The ENSURESEC project has received funding from the European Union’s Horizon 2020 research and innovation program. It aims to protect the whole range of modern e-Commerce by addressing a wide variety of threats. More specifically, it focuses on a wide variety of products ranging from virtual products and services purchased online to physical products bought online and delivered to the customers and aims at addressing numerous threats ranging from e-Commerce web applications attacks to frauds committed by customers or insiders, delivery issues, etc. [34 ].
The aforementioned toolset offers advanced monitoring capabilities aiming to ensure that the communication protocols in use, as well as the underlying communication infrastructure, function properly and safely. In this direction, certifiably correct verification methodologies (e.g., Decision Tree, Random Forest, KNN, Support Vector Machine, Voting Ensemble) are adopted, and various functionalities are included in the toolset. One of these functionalities is called Threat and Incident Detection and utilizes parts of the methodology described in Section 4. Through this functionality, ENSURESEC users can identify malicious operations or threats at the network level by analyzing, filtering and matching semantically low-level events. Furthermore, they can gain insights into the structured relationships among the various types of items involved.
Advanced threats detection mechanisms are of vital importance for well-designed and secured e-Commerce systems in order to protect and ensure sensitive data that is being targeted by malicious users. Within this scope, the current study evaluates the performance of distinct ML algorithms in Section 5 and provides an additional countermeasure component (Threat and Incident Detection) by engaging two domains of high interest (i.e., ML and cybersecurity). This will help to enhance the analysis of threats patterns and learning from this process in order to detect, prevent and recognize similar types of attacks, enhancing the capabilities of cybersecurity teams to respond in real or near-real time to active types of cyber-attacks.
Five more functionalities were included in the so-called “Communication Monitor” toolset namely:

A Threat Objects Fusion functionality through which users can fuse different objects into a unified object

A Similarity Degree Calculation feature that enables users to execute a character-by-character complex comparison algorithm among all the types of objects stored in or retrieved from a specific Knowledge Base, after the execution of certain processes

An Association Rule Engine that can be used for revealing hidden patterns and relations while exploring a populated database

A Visualization functionality that can be used for the interactive representation of populated ontologies in the form of graphs

An Advanced Reasoner through which users can apply rule-based logical reasoning into the existing Knowledge Base

Further analysis of these functionalities is beyond the scope of the current research work.
The Threat and Incident Detection functionality, together with the other functionalities of the Communication Monitor, are depicted in Figure 2.
Peppes N., Daskalakis E., Alexakis T., Adamopoulou E, & Demestichas K. (2021). Performance of Machine Learning-Based Multi-Model Voting Ensemble Methods for Network Threat Detection in Agriculture 4.0. Sensors (Basel, Switzerland), 21(22), 7475.
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Publication 2021
Character Dystocia Pharmaceutical Preparations Physical Examination physiology
5
Anti-inflammatory Treatment in Postpartum Dairy Cows
This non-randomized controlled trial was conducted from April to August 2018 at the University of Guelph Livestock Research and Innovation Centre, Dairy Facility (Elora, ON, Canada). Animal procedures were approved by the University of Guelph Animal Care Committee, and cows were managed according to the guidelines set by the Dairy Farmers of Canada and the National Farm Animal Care Council. The sample size calculation was based on the effect of anti-inflammatory treatment using 80% power and a 95% confidence level. The sample size of 20 cows per group was based on detection of differences of 0.5 ± 0.5 g/L (mean ± SD) in serum haptoglobin or 10 ± 10% points of PMN in endometrial cytology samples. Only cows considered clinically healthy from calving to 35 days postpartum were included (having unassisted calving, and no retained placenta, metritis or other clinical disease before or during the study period). Initially, 59 non-lactating pregnant Holstein cows (24 nulliparous and 35 parous) were enrolled 14 days before the expected calving date (266 days after the last insemination). Seventeen cows were excluded from the experiment due to injury in the prepartum period (n = 1), dystocia (n = 7), retained placenta (n = 3), or clinical disease (n = 6; metritis, displaced abomasum, or ketosis). The final sample consisted of 42 healthy Holstein cows (17 nulliparous and 25 parous; parity 1.3 ± 1.3), with an average body condition score (BCS) at inclusion of 3.8 ± 0.2 out of 5. Dry cows were housed in free-stall pens and moved to individual calving pens 48 hours before expected calving, or if presenting external signs of preparation for calving (e.g., swelling of the vulva and relaxation of the pelvic ligaments). Five days after calving, healthy cows were relocated to a free-stall lactating pen, where they remained until the end of the experiment (35 days postpartum). In the free-stall pens, daily feed DMI was measured by individual automated feed bins (Insentec B.V., Marknesse, Netherlands). In the individual maternity pen, the amount of offered and refused feed was weighed daily by trained farm personnel. The diets and chemical composition of the lactating cow total mixed ration are described in Supplementary Table A. Cows were fed once daily (1300 h) for ad libitum intake after cleaning out of each feed bin. Milking was carried out twice daily (0500 and 1700 h) in a rotary parlour. At inclusion, cows were assigned to one of two experimental treatment groups to deliberately balance for parity and BCS. Experimental groups consisted of control (CON, n = 22; parity 1.3 ± 1.7 and BCS 3.8 ± 0.2) and meloxicam (MEL, n = 20; parity 1.2 ± 1.3 and BCS 3.9 ± 0.2) treatments. MEL received subcutaneous injections of meloxicam (0.5 mg/kg of body weight; Metacam, Boehringer Ingelheim Canada Ltd., Burlington, ON, Canada) once daily on 10, 11, 12 and 13 days postpartum (between 1000 and 1100).
Pascottini O.B., Van Schyndel S.J., Spricigo J.F., Carvalho M.R., Mion B., Ribeiro E.S, & LeBlanc S.J. (2020). Effect of anti-inflammatory treatment on systemic inflammation, immune function, and endometrial health in postpartum dairy cows. Scientific Reports, 10, 5236.
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Publication 2020
Abomasum Animal Care Committees Animals Anti-Inflammatory Agents Body Weight Cattle chemical composition Cytological Techniques Cytomegalovirus Infections Diet Dystocia Endometrium Farm Animals Farmers Haptoglobins Holstein Cow Human Body Injuries Insemination Ketosis Ligaments Livestock Meloxicam Pelvis Placenta, Retained Serum Subcutaneous Injections Therapies, Investigational Vulva

Most recents protocols related to «Dystocia»

1
Pregnancy Outcomes and Maternal Factors
Data were collected from the electronic information system of health centers of Mashhad University of Medical Sciences. These data included related demographic characteristics (age, education, and occupation), past medical & social histories, pregnancy relate data (gravidity, parity, history of previous delivery problems), and health status (body mass index (BMI) before pregnancy, gestational weight gain (GWG)). Collected maternal outcomes of pregnancy were gestational diabetes, gestational hypertension, delivery mode, hospitalization, blood transfusion, and postpartum preeclampsia. Evaluated fetal pregnancy outcomes included preterm birth, low birth weight and macrosomia.
Davaryari N., Davaryar S., Azarshab A., Vakilzadeh M.M., Vakili V, & Moazzeni Z. (2023). Comparison of maternal and fetal health outcomes in the pandemic period of covid-19 with the same last year duration in health centers of second largest city of Iran: A population-based cohort study. Heliyon, 9(3), e14439.
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Publication 2023
Blood Transfusion Care, Prenatal Dystocia Gestational Diabetes Hospitalization Index, Body Mass Mothers Obstetric Delivery Pre-Eclampsia Pregnancy Premature Birth Transient Hypertension, Pregnancy
2
Collaborative Delivery Optimization for Multi-Store Retailers
The “Multi-Store Collaborative Delivery Optimization (MCDO)” problem can be described as follows: Consider there is a retailer with several physical stores in a region. Each store has order fulfillment ability and can collaborate to deliver orders. Each multi-item order can be split into sub-orders based on the storage of each store, as well as the distance between the store and the order. For each item in the order, it can be delivered directly from the store to the customer or transferred to another store for consolidation and deliver to customers with the other items. Use the following example to explain the MCDO problem.
Example 1. Tables 1 and 2 show the information of the order and store, including the arrival time, the delivery time window, and the demand item of the order, and Table 3 shows the distance between the order and the store. Fig 1 shows the location of each store and order, the straight-line distance on the graph represents the delivery time and delivery cost of an order. Denote store and order by m and r respectively, and use am and ar to denote the items that the store owned and the order required, now there are four stores m1, m2, m3, m4, and two dynamically arriving online orders r1, r2. It is important to match real-time orders with stores, i.e., select stores that can complete the orders at a minimum cost, and design the order delivery routes for stores.
At time 0, order r1 arrives, it can be delivered by {m1, m2} and {m3} for two solutions, as shown in Fig 2. The cost of solutions is 4 and 5, and the delivery time of solutions is 3 and 5 respectively. Both solutions satisfy the time window of r1, thus the optimal split plan of r1 is, therefore {m1, m2}. And with the consolidation of m1 and m2, the joint delivery route Sv = {m2, m1, r1}, where the cost and the delivery time are both 3, which is better than the individual delivery, it’s the optimal delivery solution of r1 in the offline scenario.
At the moment 2 r2 arrives, the optimal order-split plan of r2 is {m3, m4}. In this situation, if r1doesn’t deliver, with the consolidation of stores, the solution of r1 can be change from {m1, m2} to {m3}. In this way, the total cost of delivering {r1, r2} reduced from 9 to 8, and the delivery time of r1 is 7, r2 is 8, which both satisfy the time window, the final delivery plan of r1 and r2 can be seen in Fig 3. It can be imagined that as the scale of orders expands, the route repetition rate between different solutions will rise. Through the joint optimization of order-split and order-delivery, the route repetition rate can be effectively reduced, which is conducive to improving the efficiency of order fulfillment and reducing the order fulfillment cost of the enterprise.
Zhang Y., Ou L, & Liu J. (2023). Multi-store collaborative delivery optimization based on Top-K order-split. PLOS ONE, 18(3), e0278690.
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Publication 2023
Dystocia Joints Obstetric Delivery Physical Examination Satisfaction Vision
3
Hybrid Heuristic for Optimizing Order Fulfillment
Jasin has proved that the order-split and allocation problem is an NP-hard problem [12 ], and the delivery route problem is also a typical NP-hard problem, so the MCDO problem is an NP-hard problem as well. While the exact algorithm takes too much time to solve the problem and is usually only applicable to small-scale problems, the heuristic algorithm can find a satisfactory solution in a short time. And is suitable for large-scale order fulfillment situations, which also has broader applicability.
To this end, a hybrid heuristic (TKILS) algorithm is designed to solve the MCDO problem. The algorithm consists of order-split, order delivery, and joint optimization. In the order-split part, use an improved breadth-first search to reduce the solution space of the problem. In the order-delivery part, use a greedy cost function to generate the initial delivery route. And finally, optimize order-split and order-delivery simultaneously with improved local search.
The specific processes of TKILS are as follows: Firstly, based on the store’s inventory and orders information, the minimum number of split orders and the shortest distance are combined to obtain the Top-K order-split plans. Each order’s Top-1 order-split plan determines the initial order allocation plan. Secondly, according to the initial order allocation plan, the greedy cost function generates the initial order-delivery route. An improved local search algorithm is then used to explore the new neighbourhood space for the remaining Top-K order-split plans. All solutions are stocked in the alternative pool (POOL) to obtain more feasible neighbour solutions. Finally, if no new orders arrive, the satisfactory solution is output directly. If there is a new order, then the Top-K operation is performed on the new order and matched with the solutions in POOL to obtain a new satisfactory solution.
Zhang Y., Ou L, & Liu J. (2023). Multi-store collaborative delivery optimization based on Top-K order-split. PLOS ONE, 18(3), e0278690.
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Publication 2023
Dystocia Hybrids Infantile Neuroaxonal Dystrophy Joints Obstetric Delivery
4
Assessing Clinical Quality in Maternal Care
We constructed a clinical quality index (possible range from 0 to 22) that captures women’s individual experience of clinical quality. This index combines 22 items that ask each participant whether she received certain procedures or services such as blood pressure, pulse checks, or vaginal examinations during her stay at the facility. Secondary outcomes investigated included how frequently a doctor/nurse visited the participant in the postnatal ward, occurrence of delivery problems, participants’ overall satisfaction with the services received, participants’ quality of care rating, and participants’ intention to use a family planning method in the next six months. We examined socio-demographic factors, pregnancy factors, and facility and provider characteristics that may be associated with PCMC and various outcomes. Socio-demographic factors included age, parity, marital status, age at marriage, age at first pregnancy, number of pregnancies, employment, religion, national wealth quintile, education, and literacy. Pregnancy characteristics such as number of antenatal care visits and problems during pregnancy were also examined. Facility characteristics included facility type, delivery provider gender, type of delivery provider, and the facility’s baseline clinical quality. Baseline clinical quality was captured by the mean clinical quality index score of each facility’s baseline sample.
Sudhinaraset M., Giessler K.M., Nakphong M.K., Munson M.M., Golub G.M., Diamond-Smith N.G., Opot J, & Green C.E. (2023). Can a quality improvement intervention improve person-centred maternity care in Kenya?. Sexual and Reproductive Health Matters, 31(1), 2175448.
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Publication 2023
Blood Pressure Care, Prenatal Dystocia Gender Nurses Obstetric Delivery Physicians Pregnancy Pulse Rate Quality of Health Care Satisfaction Vaginal Examination Woman
5
Episiotomy and Prelabor Cesarean Delivery
Analyses were performed in SPSS 26.0 (IBM Corp, Armonk, NY, USA). Firstly we constructed a directed acyclic graph to establish possible confounders and mediators (Figure S1). Then we explored differences between women exposed to episiotomy or not in the first birth by VE using test of proportions, based on the directed acyclic graph. We considered differences with a p‐value of <0.10 as possible confounders since these covariates had a 90% likelihood or more of being associated with the outcome (Table 1). Factors analyzed but omitted from Table 1 due to nonsignificant differences were smoking, cohabitation, hypertension and preeclampsia (data not presented).
Secondly, we assessed risk factors for prelabor cesarean delivery in the second birth using covariates from Table 1 in univariate and multivariate regression models. Results are presented as crude and adjusted odds ratios (aOR) with 95% confidence intervals (95% CI) in Table 2. The multivariate model included all factors significant in the univariate analysis: maternal age, country of birth, education, gestational age, epidural, labor dystocia, intrapartum fetal distress, station, head position, head circumference, birthweight, shoulder dystocia, Apgar 1 min <4, Apgar 1 min <7, episiotomy, year of delivery and region of delivery.
Thirdly, the propensity score (the conditional probability of being assigned episiotomy or not) was calculated using all covariates with a p‐value of <0.10 in Table 1: maternal age, country of birth, maternal height, maternal BMI, higher education, gestational age, epidural, labor dystocia, intrapartum fetal distress, fetal station, head position, head circumference, birthweight, shoulder dystocia, Apgar 1 min <4, Apgar 1 min <7, year of delivery and region of delivery. The propensity score was then used to perform a regression analysis and to calculate an inversed probability of treatment (episiotomy) weight for each individual as initially described by Rosenbaum.18 We used a modified computer syntax for SPSS provided by Thoemmes et al.19 The weight was used to account for bias due to observed confounders creating a pseudo‐population in which the covariates and the treatment assignment (episiotomy or not) are independent of each other, to mimic a randomized treatment assignment.20, 21, 22 We assessed the outcome using all obtained stabilized weights, as well as truncated stabilized weights, at the 5th and 95th percentiles or the 1st and 99th percentiles.
Fourthly, since episiotomy and OASIS are associated, we explored the prevalence and association of prelabor cesarean delivery in the second birth in women with four principal groups of exposure: “neither episiotomy nor OASIS”, “episiotomy, no OASIS”, “OASIS, no episiotomy” and “both OASIS and episiotomy”, using “neither episiotomy nor OASIS” as reference. The association was tested using multivariate logistic regression adjusting for maternal age, country of birth, higher education, gestational age, epidural, labor dystocia, intrapartum fetal distress, station, head position, head circumference, birthweight, shoulder dystocia, Apgar at 1 min, year of delivery, and region of delivery. Moreover, interaction between episiotomy and OASIS was formally tested using multivariate logistic regression entering the interaction term “episiotomy*OASIS”, “episiotomy”, “OASIS”, and all the confounders used in the multivariate model.
Brismar Wendel S., Liu C, & Stephansson O. (2023). The association between episiotomy or OASIS at vacuum extraction in nulliparous women and subsequent prelabor cesarean delivery: A nationwide observational study. Acta Obstetricia et Gynecologica Scandinavica, 102(3), 378-388.
Publication 2023
Birth Birth Weight Care, Prenatal Cesarean Section CREB3L1 protein, human Dystocia Episiotomy Fetal Distress Gestational Age Head High Blood Pressures Mothers Obstetric Delivery Pre-Eclampsia Shoulder Dystocia Woman

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More about "Dystocia"

Dystoccia, also known as difficult or abnormal childbirth, is a complication that can arise during labor and delivery.
This condition is characterized by challenges in the birthing process, which may be caused by factors such as a large fetus, abnormal fetal positioning, or pelvic abnormalities.
Prompt recognition and appropriate management of Dystoccia are crucial to ensure the safety and well-being of both the mother and the baby.
Researchers studying Dystoccia can benefit from the advanced tools and capabilities offered by PubCompare.ai's AI-driven platform.
This platform provides easy access to relevant protocols from the literature, preprints, and patents, while utilizing intelligent comparisons to identify the best approaches.
By streamlining the research process, PubCompare.ai's platform can help researchers in the field of Dystoccia studies to enhance reproducibility and accuracy.
In addition to Dystoccia, other related terms and concepts that may be relevant to this research area include Tamoxifen (TMX), a drug used in the treatment of certain types of breast cancer, AU5800, a specific model of Tamoxifen, SPSS v20, a statistical software package, EOS 650D, a camera model, DMEM/F12, a cell culture medium, Metacam, a non-steroidal anti-inflammatory drug, and Ansys Electromagnetics Suite 17-HFSS, a software tool for electromagnetic simulations.
Incorporating these related terms and concepts can help provide a more comprehensive understanding of the broader context surrounding Dystoccia research.
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